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Novel sparse auto-encoder framework with pseudo-labeled reinforcement for cross domain fault diagnosis with imbalanced samples

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MEASUREMENT SCIENCE AND TECHNOLOGY 2025年第1期36卷

作者： Zhang, Faye Liu, Fuzheng Liu, Minghui Zhang, Yilan Jiang, Mingshun Sui, Qingmei Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China

By applying diagnostic expertise from one area to another that is closely related, transfer fault diagnosis is an efficient strategy for guaranteeing the secure and dependable operation of mechanical equipment. However, the majority of rotating machinery monitoring data are gathered in industrial applications under normal operating settings, which leads to an imbalance between positive and negative samples. Consequently, performing high-precision fault diagnosis with imbalanced data and different working conditions becomes challenging due to the escalating difficulty and cost of acquiring labeled fault samples. For cross domain fault diagnosis, an enhanced deep transfer sparse auto-encoder (SAE) framework is provided. This approach leverages a SAE to delve deeper into fault features within imbalanced data and reconstruct the samples accordingly. Furthermore, the research proposes a feature domain penalty term to facilitate cross-domain training by aligning the distribution of the source and target domain data which can reduce data distribution deviation. A pseudo-labeled reinforcement training method is presented to further improve cross-domain classification accuracy with imbalanced samples. Extensive experiments were conducted on two datasets to assess the proposed method. The results were compared with other algorithms, demonstrating the effectiveness and superiority of the proposed approach.

关键词： pseudo-labeled-reinforcement training rolling bearing fault diagnosis sparse auto-encoder transfer learning.

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Optimization of stamping process parameters based on an improved particle swarm optimization-genetic algorithm and sparse auto-encoder-back-propagation neural network model

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ENGINEERING OPTIMIZATION 2024年第2期56卷 252-273页

作者： Xie, Yanmin Liu, Cheng Li, Wei Du, Meiyu Feng, Kai Southwest Jiaotong Univ Sch Mech Engn Chengdu Peoples R China

Stamping is a very important manufacturing process. To optimize the process parameters, a hybrid surrogate model based on the back-propagation neural network and sparse auto-encoder is proposed and compared with classical surrogate models to verify its reliability. Furthermore, the hybrid improved particle swarm optimization-genetic algorithm, based on chaos theory, is proposed and compared with other algorithms. A double-C part is used as an engineering example to verify the proposed method. The Latin hypercube sampling method is used for sampling and the response value is obtained by autoForm simulation software. On this basis, the hybrid surrogate model is used to establish the mapping relationship between the forming quality of the double-C part and the stamping process parameters. The optimal stamping process parameters are obtained through the improved hybrid algorithm. The results demonstrate that the wrinkling of the optimized double-C part is significantly reduced and the forming quality is improved.

关键词： Stamping sparse auto-encoder neural network hybrid algorithm forming quality

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sparse auto-encoder combined with kernel for network attack detection

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COMPUTER COMMUNICATIONS 2021年 173卷 14-20页

作者： Han, Xiaolu Liu, Yun Zhang, Zhenjiang Lu, Xin Li, Yang Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 CO Peoples R China Beijing Jiaotong Univ Sch Software Engn Beijing 100044 CO Peoples R China State Informat Ctr Postdoctoral Sci Workstat Beijing 100045 CO Peoples R China

In this study, we propose sparse auto-encoder combined with kernel for network attack detection for better network security. High-dimensional data seriously affects the accuracy and efficiency of network attack detection, leading to dimension disaster and model over fitting. To address this problem, we optimize the sparse auto-encoder with combined kernel to reconstruct the data features of network attack. Besides, we used the iterative method of adaptive genetic algorithm to optimize the objective function of sparse auto-encoder with combined kernel. The feature matrix after dimension reduction is obtained by sparse auto-encoder with combined kernel, which solves the dimensional reduction problem of nonlinear features and sparse features of network attack. The proposed model improves the efficiency of network attack detection. The simulation using experimental data based on botnet attack detection data set of the Internet of things(IOT) show that, compared with the traditional feature extraction algorithm and other deep learning feature extraction methods, the recognition rate based on sparse auto-encoder method with combined kernel for network attack detection can reach 98.68%, and the average dimension reduction time is 5.59 s, which depicts better recognition rate and computational efficiency.

关键词： Big data Feature extraction sparse auto-encoder Kernel function Network attack detection

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A novel SSD fault detection method using GRU-based sparse auto-encoder for dimensionality reduction

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第4期43卷 4929-4946页

作者： Wang, Yufei Dong, Xiaoshe Wang, Longxiang Chen, Weiduo Chen, Heng Xi An Jiao Tong Univ Xian Ning West Rd 28 Xian 710049 Shaanxi Peoples R China

In recent years, with the development of flash memory technology, storage systems in large data centers are typically built upon thousands or even millions of solid-state drives (SSDs). Therefore, the failure of SSDs is inevitable. An SSD failure may cause unrecoverable data loss or unavailable system service, resulting in catastrophic results. Active fault detection technologies are able to detect device problems in advance, so it is gaining popularity. Recent trends have turned toward applying AI algorithms based on SSD SMART data for fault detection. However, SMART data of new SSDs contains a large number of features, and the high dimension of data features results in poor accuracy of AI algorithms for fault detection. To tackle the above problems, we improve the structure of traditional auto-encoder (AE) based on GRU and propose an SSD fault detection method - GAL based on dimensionality reduction with Gated Recurrent Unit (GRU) sparse autoencoder (GRUAE) by combining the temporal characteristics of SSD SMART data. The proposed method trains the GRUAE model with SSD SMART data firstly, and then adopts the encoder of GRUAE model as the dimensionality reduction tool to reduce the original high-dimensional SSD SMART data, aiming at reducing the influence of noise features in original SSD SAMRT data and highlight the features more relevant to data characteristics to improve the accuracy of fault detection. Finally, LSTM is adopted for fault detection with low-dimensional SSDSMART data. Experimental results on real SSD dataset from Alibaba show that the fault detection accuracy of various AI algorithms can be improved by varying degrees after dimensionality reduction with the proposed method, and GAL performs best among all methods.

关键词： Fault detection dimensionality reduction sparse auto-encoder solid state drives gated recurrent unit

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A Deep Learning Approach to Network Intrusion Detection Using a Proposed Supervised sparse auto-encoder and SVM

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING 2022年第3期46卷 829-846页

作者： Ghorbani, Ali Fakhrahmad, Seyed Mostafa Shiraz Univ Sch Elect & Comp Engn Dept Comp Sci & Engn & IT Shiraz Iran

Due to the increasing use of communication technologies for data transmission, security threats have increased over the past decade. One of the essential solutions to detect threats is NIDSs. Over the past few years, much research considered unsupervised feature extraction for NIDS like sparse auto-encoders;however, there is no research on the supervised auto-encoder methods. In this work, we propose a novel supervised sparse auto-encoder, which aims to extract more useful information for classification models than unsupervised methods. The proposed approach validated using NSL-KDD, KDDCUP'99, CICIDS2017 data-sets, in the term of detection rate, and also response time. Experimental results in detection rate and test time are promising. In the case of binary classification, the accuracy of 90. 11% on NSL-KDDTest(+) and 91.21 on CICIDS2017 were achieved, which is a drastic improvement compared to state of the art with the more complex models and unsupervised representation learning models. Also, the result on 5class classification was considered.

关键词： Network intrusion detection Deep learning sparse auto-encoder Supervised auto-encoder

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Integrating Stacked sparse auto-encoder Into Matrix Factorization for Rating Prediction

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IEEE ACCESS 2021年 9卷 17641-17648页

作者： Zhang, Yihao Zhao, Chu Chen, Mian Yuan, Meng Chongqing Univ Technol Sch Artificial Intelligence Chongqing 400054 Peoples R China

Currently, collaborative filtering technology has been widely used in personalized recommender systems. The problem of data sparsity is a severe challenge faced by traditional collaborative filtering methods based on matrix factorization techniques. A lot of improved collaborative filtering methods have been proposed to alleviate the data sparsity problem;However, due to the sparsity of the user rating matrix, the latent factor learned by these improved methods may be not efficient. In this paper, we propose a novel recommendation algorithm named SSAERec by integrating stacked sparse auto-encoder into matrix factorization for rating prediction, which can learn effective representation from user-item rating matrix. Extensive experiments on three real-world datasets demonstrate the proposed method outperforms other baselines in the rating prediction task.

关键词： sparse matrices Collaboration Recommender systems Feature extraction Deep learning Task analysis Data models sparse auto-encoder collaborative filtering data sparsity rating prediction

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Deep sparse auto-encoder Features Learning for Arabic Text Recognition

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IEEE ACCESS 2021年 9卷 18569-18584页

作者： Rahal, Najoua Tounsi, Maroua Hussain, Amir Alimi, Adel M. Tunis El Manar Univ Fac Sci Tunis Tunis 2092 Tunisia Univ Sfax Natl Engn Sch Sfax ENIS REs Grp Intelligent Machines REGIM Lab Sfax 3038 Tunisia Edinburgh Napier Univ Sch Comp Edinburgh EH10 5DT Midlothian Scotland

One of the most recent challenging issues of pattern recognition and artificial intelligence is Arabic text recognition. This research topic is still a pervasive and unaddressed research field, because of several factors. Complications arise due to the cursive nature of the Arabic writing, character similarities, unlimited vocabulary, use of multi-size and mixed-fonts, etc. To handle these challenges, an automatic Arabic text recognition requires building a robust system by computing discriminative features and applying a rigorous classifier together to achieve an improved performance. In this work, we introduce a new deep learning based system that recognizes Arabic text contained in images. We propose a novel hybrid network, combining a Bag-of-Feature (BoF) framework for feature extraction based on a deep sparse auto-encoder (SAE), and Hidden Markov Models (HMMs), for sequence recognition. Our proposed system, termed BoF-deep SAE-HMM, is tested on four datasets, namely the printed Arabic line images Printed KHATT (P-KHATT), the benchmark printed word images Arabic Printed Text Image (APTI), the benchmark handwritten Arabic word images IFN/ENIT, and the benchmark handwritten digits images Modified National Institute of Standards and Technology (MNIST).

关键词： Text recognition Feature extraction Hidden Markov models Image segmentation Dictionaries Visualization Optical character recognition software Arabic text recognition feature learning bag of features sparse auto-encoder hidden Markov models

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sparse auto-encoder with regularization method for health indicator construction and remaining useful life prediction of rolling bearing

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MEASUREMENT SCIENCE AND TECHNOLOGY 2020年第10期31卷 105005-105005页

作者： She, Daoming Jia, Minping Pecht, Michael G. Southeast Univ Sch Mech Engn Nanjing 211189 Peoples R China Univ Maryland Ctr Adv Life Cycle Engn College Pk MD 20740 USA

Remaining useful life (RUL) prediction, allowing for mechanical predictive maintenance, reduces unplanned and expensive maintenance greatly. One of the great challenges of data-driven RUL prediction is to extract the features that describe the actual degradation process. This paper presents a health indicator (HI) construction method based on a sparse auto-encoder with regularization (SAEwR) model for rolling bearings. This paper includes two modules, HI construction and RUL prediction. In the stage of the HI construction, the original features are compressed and extracted by the SAEwR model. The extracted features are sorted according to the trendability, and the features with large trendability are selected to construct the HI by using minimum quantization error. In the module of RUL prediction, the maximum likelihood estimation method is used to estimate the parameters of the prediction model, and a particle filter-based RUL prediction with degradation model is proposed. The proposed method is benchmarked with variational auto-encoder, auto-encoder methods and principal component analysis. The data from PRONOSTIA and ABLT-1A platform support the value of our approach.

关键词： deep learning sparse auto-encoder regularization health indicator RUL prediction particle filter

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Imbalanced Fault Diagnosis Based on Particle Swarm Optimization and sparse auto-encoder 24

Imbalanced Fault Diagnosis Based on Particle Swarm Optimizat...

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24th IEEE International Conference on Computer Supported Cooperative Work in Design (IEEE CSCWD)

作者： Peng, Peng Zhang, Wenjia Zhang, Yi Wang, Hongwei Zhang, Heming Tsinghua Univ Beijing Peoples R China Zhejiang Univ Hangzhou Peoples R China

ISBN: (纸本)9781728165974

Imbalanced fault diagnosis becomes increasingly im-portant as the number of fault samples is relatively small in practical situations. sparse auto-encoder(SAE) has been well addressed in fault diagnosis while it is not suitable for imbalanced fault diagnosis. Cost sensitive learning can be uti-lized to extend the sparse auto-encoder to cost sensitive sparse auto-encoder(CS-SAE). However, the class weights assigned to different classes are usually unknown in practice. So we propose to use particle swarm optimization(PSO) to optimize the class weights for cost sensitive sparse auto-encoder(PSO-CSSAE). The experiments have shown that the proposed ap-proach consistently outperforms the state of the art on Tennesse Eastman(TE) dataset.

关键词： deep learning fault diagnosis imbalanced learning sparse auto-encoder particle swarm optimization

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An unsupervised online monitoring method for tool wear using a sparse auto-encoder

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2020年第5-6期106卷 2493-2507页

作者： Dou, Jianming Xu, Chuangwen Jiao, Shengjie Li, Baodong Zhang, Jilin Xu, Xinxin Changan Univ Sch Construct Machinery Xian 710064 Peoples R China Lanzhou Inst Technol Prov Key Lab Green Cutting Technol & Applicat Gan Lanzhou 730050 Peoples R China

Tool wear, and its online monitoring, plays an important role in increasing productivity and improving product quality. We describe an unsupervised method to monitor the wear state of milling cutter by tracking an error sequence generated by reconstructing monitoring signals from a sparse auto-encoder (SAE). The monitoring signals consist of the force and vibration signals collected during the cutting process. We establish a well-structured SAE model, which can adaptively extract the characteristics of the signal and complete the training of the model without supervision of the empirical label and investigate the reconstruction performance of the model for cutting signal. On this basis, an automatic online tool wear state identification strategy is designed to monitor the milling process. The mean reconstruction error (MRE) sequence associated with tool wear is recorded in real time by reconstructing the next signal segment from the SAE model, which is trained and updated using the current signal segment. Monitoring criteria and thresholds are recommended to automate the identification of tool wear conditions based on the filtered MRE curve. Five experiments with two different milling environments are run to confirm the feasibility of tool wear monitoring using this method, and the results show that the method can be used to monitor tool wear conditions online under different milling conditions without being supervised by any empirical labels.

关键词： Tool wear Online monitoring sparse auto-encoder Unsupervised Force signal Vibration signal

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